Immune thrombocytopaenic purpura in pregnancy: a case of near miss mortality in a Nigerian

Thrombocytopenia occurs in pregnancy like in the non-pregnant state and can be due to immune thrombocytopaenic purpura (ITP). The hyperoestrogenic state of pregnancy has been identified as a precipitating factor. This is a case report of a thirty year old Nigerian lady, who at a gestational age of 26 weeks developed ITP as a near miss mortality. Although, most literatures reported that the perinatal outcome is usually favourable in this condition, we report a case managed in our facility that had intrauterine death and non- remission until delivery; despite corticosteroid therapy and transfusion of eleven (11) units of blood. This report is relevant in a developing world where a rare condition almost caused a maternal death in spite of the high maternal mortality rates from other conditions. Baseline full blood count is advocated at booking to identify and monitor rare haematological disorders like this in pregnancy. (Immune thrombocytopaenic purpura, Nigeria, perinatal mortality, pregnancy)Trop J Obstet Gynaecol, 30 (1), April 201

HISTOGRAM NORMALIZATION TECHNIQUE FOR PREPROCESSING MAMMOGRAPHIC IMAGES

images requires high computational capabilities. Pre-processing is one of the most important step in the mammogram analysis due to poor captured mammographic image qualities. Pre-processing is basically used to correct and adjust the mammogram image for further study and classification. Many image pre-processing techniques have been developed over the past decades to help radiologists in diagnosing breast cancer. Most studies conducted have proven that a pre-processed image is easier for radiologist to accurately detect breast cancer especially for dense breast. Different types of techniques are available for preprocessing of mammograms, which are used to improve image quality, remove noise, adjust contrast, enhance the image and preserve the edges within the image. This paper acquired 20 digital mammograms from Mammographic Image Analysis Society (MIAS) database and uses Histogram Normalization algorithm for pre-processing of the mammograms. A percentage of 95% was obtained. It was observed that the pre-processed mammographic images displayed breast abnormalities clearer with little or no noise